Patterns of leaf biochemical and structural properties of Cerrado life forms: Implications for remote sensing

Aaron Ball, Arturo Sanchez-Azofeifa, Carlos Portillo-Quintero, Benoit Rivard, Saulo Castro-Contreras, Geraldo Fernandes

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Aim: The general goal of this study is to investigate and analyze patterns of ecophysiological leaf traits and spectral response among life forms (trees, shrubs and lianas) in the Cerrado ecosystem. In this study, we first tested whether life forms are discriminated through leaf level functional traits.We then explored the correlation between leaf-level plant functional traits and spectral reflectance. Location: Serra do Cipo National Park, Minas Gerais, Brazil. Methods: Six ecophysiological leaf traits were selected to best characterize differences between life forms in the woody plant community of the Cerrado. Results were compared to spectral vegetation indices to determine if plant groups provide means to separate leaf spectral responses. Results: Values obtained from leaf traits were similar to results reported from other tropical dry sites. Trees and shrubs significantly differed from lianas in terms of the percentage of leaf water content and Specific Leaf Area. Spectral indices were insufficient to capture the differences of these key traits between groups, though indices were still adequately correlated to overall trait variation. Conclusion: The importance of life forms as biochemical and structurally distinctive groups is a significant finding for future remote sensing studies of vegetation, especially in arid and semi-arid environments. The traits we found as indicative of these groups (SLA and water content) are good candidates for spectral characterization. Future studies need to use the full wavelength (400 nm-2500 nm) in order to capture the potential response of these traits. The ecological linkage to water balance and life strategies encourages these traits as starting points for modeling plant communities using hyperspectral remote sensing.

Original languageEnglish
Article numbere0117659
JournalPloS one
Volume10
Issue number2
DOIs
StatePublished - Feb 18 2015

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